Real-time service provider progress monitoring

ABSTRACT

A network computer system provides a service instruction to a computing device. The service instruction can include offers, such as a service request to pick up and transport a user, and recommendations, such as a movement recommendation encouraging the service provider to relocate to another geographic area. The network computer system remotely monitors the computing device to receive a current position of the computing device as the service provider travels within a geographic area. The network computer system remotely monitors the computing device to receive a service state of the service provider. The network computer system periodically determines whether the service provider is making progress towards a target of the service instruction based on the current position of the computing device and a set of progress conditions, including determining whether the service provider satisfied the set of progress conditions in response to a change in the service state.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Patent Application No.62/591,037, filed Nov. 27, 2017, which priority application is herebyincorporated by reference in its entirety.

BACKGROUND

A network service can enable users to request and receive variousservices, such as ride-sharing, through applications on mobile computingdevices. The network service selects one of many service providers tofulfill the request based on user-specified data from the request. Theseservice providers can interact with the network service to accept ordecline service requests, receive data about the requesting users, andset various service states such as whether the provider is offline oronline and available to fulfill requests.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure herein is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements, and in which:

FIG. 1 is a block diagram illustrating an example network computersystem in communication with user and transport provider computingdevices, according to examples described herein;

FIGS. 2A and 2B are block diagrams illustrating an example taskregistration architecture, according to examples described herein;

FIG. 3 is a block diagram illustrating an example task processingarchitecture, according to examples described herein;

FIG. 4 is a block diagram illustrating a shrinking circle attractionmodel used for real-time progress monitoring, according to examplesdescribed herein;

FIG. 5 is a flow chart describing an example method of real-timeprogress monitoring for a transport provider, according to examplesdescribed herein;

FIG. 6 is a flow chart describing an example method of progressmonitoring in a network computer system, according to examples describedherein;

FIG. 7 is a block diagram illustrating an example computing deviceexecuting and operating a designated transport application forcommunicating with a network transport service, according to examplesdescribed herein; and

FIG. 8 is a block diagram illustrating a computer system upon whichexamples described herein may be implemented.

DETAILED DESCRIPTION

On-demand transport services (e.g., passenger transport or deliveryservices) can be managed by a network-based computing system byconnecting requesting users with service providers (e.g., drivers) thatutilize a designated transport service application. The designatedapplication can execute on a computing device of the service provider toreceive, from the network computer system, invitations to providetransport services for requesting users. A corresponding serviceapplication can execute on a computing device of the requesting user toenable the user to configure and transmit transport requests to thenetwork computer system. The network computer system can receivelocation data from the computing devices of the requesting users andservice providers to coordinate the transport services throughout thetransport service region (e.g., an urban metroplex or bounded geographicregion). For example, the network computer system can match requestingusers with proximate service providers based on an estimated time ofarrival, distance, map data, and other more intricate matching models.

A current technical problem in the field of on-demand transport is inmanaging the supply distribution of service providers throughout atransport service region to match the demand from users, an imbalance inwhich can result in local pockets of oversupply and undersupplyconditions, sometimes quite severely. Providing movement recommendationsto service providers can achieve an effect of smoothing the supplyconditions over an entire region, but creates further difficulties. Forexample, not all service providers who receive a recommendation to moveto another area will comply. They might need gas, want to stay in theircurrent area, or be stuck in traffic. In addition, the assumptionsbehind the movement recommendations should be analyzed in order toprovide more accurate recommendations that benefit both serviceproviders and users.

Another problem in the field of on-demand transport is fraud detection.In one example, in order to discourage users from cancelling rides aftera service provider has already made significant progress, the networkcomputer system could charge the user a cancellation fee in order toreimburse the service provider for their time. However, a serviceprovider could possibly abuse the cancellation fee by agreeing to pickup a user and then ignoring them.

According to examples, a real-time progress monitoring system detectsthe progress of a service provider towards a target destination ormultiple target destinations. In some examples, the monitoring isperformed using a “shrinking circle attraction engine,” which applies a“shrinking circle algorithm” to determine whether the service provideris progressing towards one of the targets or ignoring it. As the circletowards a service provider's estimated time to arrival (ETA) shrinks,the system interprets this as the service provider moving closer towardsa target. The engine operates by detecting a service provider's locationchange from one sub-region to another and recategorizing the change asan “attraction state” of the service provider (e.g., toward the target,ignoring the target, inside the sub-region with the target).

The real-time progress detection system can determine whether serviceproviders are following repositioning recommendations while offline anddetermine if a service provider is progressing toward the pickuplocation once they accept a service request, which can be used to detectservice provider fraud and attempts to game the system. For example, aservice provider may accept a request but have no intention of pickingup the requesting user so that they can get a cancellation fee. Toprevent such fraud, any cancellation fee can be waived for the user ifthe progress detection system detects that the service provider wasignoring the pickup. In other cases, the fare can be adjusted if theservice provider was not making sufficient progress towards thedestination on a ride. In some use cases, warnings can be sent to theservice provider indicating that the service provider is not progressingtowards the target. The progress detection system further provides afeedback loop for movement recommendations to service providers toimprove supply provisioning. For example, the progress detection systemcan determine what percent of service providers are following arecommendation, and if too many or too few are following, the supplyprovisioning service can adjust accordingly. The progress detectionsystem can also improve the quality of recommendations by testingassumptions about the benefits provided by the recommendations.

In some aspects, the network computer system provides to a computingdevice of a service provider over a network a service instruction. Theservice instruction can include offers, such as a service request topick up and transport a user, and recommendations, such as a movementrecommendation encouraging the service provider to relocate to anothergeographic area that is determined to offer a superior experience forthe service provider (e.g., lower idle time, more revenue, etc.).

The network computer system remotely monitors the computing device toreceive provider data corresponding to a current position of thecomputing device, as determined by a location-based resource of thecomputing device, as the service provider travels within a geographicarea. In addition, the network computer system remotely monitors thecomputing device to receive provider data corresponding to a servicestate of the service provider (e.g., open, occupied, offline, etc.).

The network computer system periodically determines whether the serviceprovider is making progress towards a target (e.g., a pick up location,movement recommendation location) of the service instruction based onthe current position of the computing device and a set of progressconditions. Responsive to a change in the service state of the serviceprovider, the network computer system determines whether the serviceprovider satisfied the set of progress conditions. In some aspects, thenetwork computer system tracks multiple movement recommendations for aservice provider and determines whether the service provider issatisfying or not satisfying each of the recommendations.

In some implementations, a progress detection engine operates as abackend service that monitors whether the service provider is makingprogress towards the target. It evaluates whether the service providermotion meets expectations based on an initial estimated time to arrival(ETA), a current remaining ETA, and the actual time elapsed since theservice provider accepted the service instruction.

In one example, the progress detection engine determines the initial ETAto the target of the service instruction from a starting position of acomputing device of a service provider within a geographic area. Theprogress detection engine remotely monitors the computing device toreceive provider data corresponding to the current position of thecomputing device, as determined by a location-based resource of thecomputing device, as the service provider travels within the geographicarea.

The progress detection engine updates a remaining ETA to the specifiedlocation from the current position of the computing device and theduration of time elapsed since the service provider accepted the serviceinstruction. The progress detection engine then compares the remainingETA, the duration of time elapsed, and the initial ETA to apredetermined threshold to determine whether the service provider isprogressing towards the specified location.

In one implementation, to track progress in real-time, the progressdetection engine reads logs of service provider location and servicestatus that are processed by upstream services. If the remainingETA+duration elapsed−initial ETA<=a threshold of the greater of 2minutes or the initial ETA*a predetermined buffer value, the serviceprovider is considered to be progressing towards the target. Otherwise,the service provider is considered to be ignoring the target. Theshrinking circle attraction engine also tracks whether the serviceprovider is inside or outside the target sub-region. Once the serviceprovider's service state changes to indicate that a given task (e.g.,pick up, drop off, movement recommendation) is complete (whethersuccessful or canceled), the progress detection system stops trackingthe service provider and logs the relevant information, including thetask, a timestamp, the service provider ID, and whether the task endedin failure or success.

Among other benefits, the network computer system can leverage progressdetection to measure the progression/attraction and base signals formovement recommendations, log statistics in real time, and build a datastore as single source of truth for essential stats related to movementrecommendation. Based on progression and core signals, the networkcomputer system can better define core metrics to measure direct andindirect impacts of movement recommendations, analyze them, andvisualize them over time. Furthermore, progress detection can act as afirst step in identifying potential issues over service providerexperience and be used in investigations of service provider behavior.

As provided herein, the terms “user” and “service requester” are usedthroughout this application interchangeably to describe a person orgroup of people who utilize a service requester application on acomputing device to request, over one or more networks, on-demandservices from a network computer system. The term “service provider” isused to describe a person utilizing a service provider application on acomputing device to provide on-demand services to the servicerequesters.

As provided herein, a computing device refers to devices includingdesktop computers, cellular devices or smartphones, personal digitalassistants (PDAs), laptop computers, virtual reality (VR) or augmentedreality (AR) headsets, tablet devices, televisions (IP Television),etc., that can provide network connectivity and processing resources forcommunicating with the system over a network. A computing device canalso correspond to custom hardware, in-vehicle devices, on-boardcomputers, etc. The computing device can also operate a designatedapplication configured to communicate with the network service.

One or more examples described herein provide that methods, techniques,and actions performed by a computing device are performedprogrammatically, or as a computer-implemented method. Programmatically,as used herein, means through the use of code or computer-executableinstructions. These instructions can be stored in one or more memoryresources of the computing device. A programmatically performed step mayor may not be automatic.

One or more examples described herein can be implemented usingprogrammatic modules, engines, or components. A programmatic module,engine, or component can include a program, a sub-routine, a portion ofa program, or a software component or a hardware component capable ofperforming one or more stated tasks or functions. As used herein, amodule or component can exist on a hardware component independently ofother modules or components. Alternatively, a module or component can bea shared element or process of other modules, programs or machines.

Some examples described herein can generally require the use ofcomputing devices, including processing and memory resources. Forexample, one or more examples described herein may be implemented, inwhole or in part, on computing devices such as servers, desktopcomputers, cellular or smartphones, personal digital assistants (e.g.,PDAs), laptop computers, VR or AR devices, printers, digital pictureframes, network equipment (e.g., routers) and tablet devices. Memory,processing, and network resources may all be used in connection with theestablishment, use, or performance of any example described herein(including with the performance of any method or with the implementationof any system).

Furthermore, one or more aspects described herein may be implementedthrough the use of instructions that are executable by one or moreprocessors. These instructions may be carried on a computer-readablemedium. Machines shown or described with figures below provide examplesof processing resources and computer-readable media on whichinstructions for implementing some aspects can be carried out orexecuted. In particular, the numerous machines shown in some examplesinclude processors and various forms of memory for holding data andinstructions. Examples of computer-readable media include permanentmemory storage devices, such as hard drives on personal computers orservers. Other examples of computer storage media include portablestorage units, such as CD or DVD units, flash or solid state memory(such as carried on many cell phones and consumer devices) and magneticmemory. Computers, terminals, network-enabled devices (e.g., mobiledevices such as cell phones) are all examples of machines and devicesthat utilize processors, memory, and instructions stored oncomputer-readable media.

Alternatively, one or more examples described herein may be implementedthrough the use of dedicated hardware logic circuits that are comprisedof interconnected logic gates. Such circuits are typically designedusing a hardware description language (HDL), such as Verilog or VHDL.These languages contain instructions that ultimately define the layoutof the circuit. However, once the circuit is fabricated, there are noinstructions, and the processing is performed by interconnected logicgates.

System Description

FIG. 1 is a block diagram illustrating an example network computersystem in communication with user and service provider devices, inaccordance with examples described herein. In various examples describedherein, the network computer system 100 can include a task service 130,a supply provisioning service 140, a progress detection engine 150, anda progress output processing service 160. The network computer system100 can further include a user device interface 125 to communicate withuser devices 195 of requesting users 197 over one or more networks 180(e.g., via an executing service application 196). The network computersystem 100 can further include a service provider device interface 115that communicates with service provider computing devices 190 of serviceproviders 193 (e.g., transport providers) over the network(s) 180 (e.g.,via an executing a service application 191). The service providers 193can each operate a transport vehicle 194 accordingly to provideon-demand transport services coordinated by the network computer system100.

According to various examples, the service providers 193 can eachutilize a service provider device 190 (e.g., a mobile computing device)to execute a transport application 191 that links the service providerdevice 190 with the service provider device interface 115 of the networkcomputer system 100. The service provider device interface 115 canaccess location data, indicating the dynamic location of the serviceprovider 193, from a location-based resource (e.g., a GPS module) of theservice provider device 190 via the executing transport application 191.In addition to receiving the service provider's location, the serviceprovider device interface 115 can transmit service requests to theservice providers 193 via the transport application 191 to enable theservice providers 193 to receive the requests and perform correspondingservices for the requesting users 197.

For example, the requesting users 197 can execute a service application196 on their user devices 195 (e.g., mobile computing devices) toconfigure and transmit a pick-up request, a food delivery request, apackage or mail delivery request, and the like. The requesting users 197can transmit the requests to the user device interface 125 of thenetwork computer system 100 over the one or more networks 180. Invarious implementations, the requesting users 197 can make on-demandtransport requests for any pick-up location and destination within thetransport service region.

When one of the service providers 193 accepts a service request, thenetwork computer system 100 can update the service state for the serviceprovider 193 to reflect an in-service status. Among other potentialservice states, service providers 193 throughout the transport serviceregion can be classified by the network computer system 100 asunavailable or offline, available or online (i.e., “open”), andin-service.

According to various examples, the network computer system 100 canpartition the transport service region into a plurality of geographicsub-regions or areas based on population, number of users, surface area,etc. In some aspects, these sub-regions (or “geos”) are hexagonallyshaped and form a lattice that covers the transport service region. Thenetwork computer system 100 can further collect and store historicalsupply/demand data for the transport service for each of the partitionedareas. The network computer system 100 can also monitor real-timesupply/demand data for each of the partitioned areas. Based on thehistorical and/or real-time data, the partitioned areas can be scoredand/or ranked, or otherwise classified as oversupplied or undersuppliedwith regards to available service providers. For example, progressivelylower scoring service areas may indicate undersupply, and the networkcomputer system 100 can initiate certain mitigating measures to moreevenly distribute transport supply (e.g., service providers that canprovide transport services) throughout the transport service region. Incertain variations, the score of a partitioned service area may also bebased on current traffic conditions within the service area and/or thesurrounding service areas.

In accordance with various implementations, the scored service areas canenable the supply provisioning service 140 to construct and update asupply distribution model for the entire transport service region. Inscoring each service area, the supply provisioning service 140 canmonitor the requesting users 197 (e.g., users that have submitted atransport request) versus service providers 193 having an availablestatus within the service area. In certain aspects, when a service areahas the same or similar number of available service providers asrequesting users, or submitted transport requests, at any given time,the service area can be classified as being in equilibrium. It iscontemplated that an equilibrium condition is ideal for the entiretransport service region, such that any transport demand conditionwithin any service area can be readily met with an equal serviceprovider supply condition. In accordance with some examples, the supplyprovisioning service 140 can generate a dynamic score for each servicearea based on current supply/demand conditions, as well as one or moreforecasted supply demand scores based on the historical data for eachsub-region (e.g., a forecasted score for five minutes, ten minutes, oran hour into the future).

Certain strategies utilized by examples described herein involve thecoordination of service providers 193 in such a manner that serviceprovider supply tends to move or migrate from oversupplied service areasto undersupplied service areas. Such strategies can involve real-timenotifications to the service providers 193 of the supply/demandconditions. Based on the model, the supply provisioning service 140 cantake measures to distribute service providers from oversupplied serviceareas to undersupplied service areas. For example, the supplyprovisioning service 140 can generate movement recommendations toservice providers in oversupplied areas that encourage those serviceproviders to relocate to undersupplied areas in order to balance thesupply and demand within the transport service region.

Based on the movement recommendations generated by the supplyprovisioning service 140, the task service 130 can register tasks totrack the progress of any movement recommendations sent to serviceproviders 193. In addition, requests for service (e.g., pick uprequests) can be processed through the user device interface 125 andresult in the task service 130 registering a task to track the progressof the selected service provider 193 towards the pick up location. Infurther examples, the task service 130 can register a task to track theprogress of the selected service provider 193 towards a drop offlocation for a user during a ride.

In some examples, the progress detection engine 150 implements ashrinking circle attraction engine, which applies a shrinking circlealgorithm to determine whether the service provider 193 is progressingtoward the target of the task or ignoring it. As the circle towards aservice provider's estimated time to arrival (ETA) shrinks, theshrinking circle attraction interprets this as the service provider 193moving closer toward the target. The engine operates by detecting aservice provider's location change from one sub-region to another andrecategorizing the change as an attraction state of the service provider193 (e.g., toward the target, ignoring the target, inside the sub-regionwith the target).

In one implementation, to track progress in real-time, the progressdetection engine 150 reads logs of service provider location and servicestate that are processed by upstream services (not illustrated). Onreceiving a location update of a service provider 193, the progressdetection engine 150 loads existing attractions related to the serviceprovider 193 from a database, and for each attraction task, updates theattraction states based on the estimated time to arrival and timeelapsed, then processes compliance updates. In some examples, theprogress detection engine 150 updates attraction states each time theservice provider 193 crosses from one sub-region to another or every 60seconds if the service provider 193 remains within one sub-region duringthat time.

On receiving a service state update for a service provider 193, theprogress detection engine 150 can update a list of service providers 193for general use cases of attraction measurement. For positioningcompliance, if the state is in “begin” status (e.g. open), the progressdetection engine 150 creates attraction states for existing attractions,tied to available tasks. If the service provider's service state changesto indicate that a given task (e.g., pick up, drop off, movementrecommendation) is complete (whether successful or canceled), theprogress detection system stops tracking the service provider and logsthe relevant information, including the task, a timestamp, the serviceprovider ID, and whether the task ended in failure or success.

In some aspects, a progress output processing service 160 can performvarious actions by processing logs of progress updates that the progressdetection engine 150 writes. In one example, the progress outputprocessing service 160 can determine whether service providers arefollowing repositioning recommendations while online and not in-service.The progress detection system further provides a feedback loop formovement recommendations to service providers to improve supplyprovisioning. For example, the progress detection system can determinewhat percent of service providers are following a recommendation, and iftoo many or too few are following, the supply provisioning service 140can adjust accordingly.

Data from the progress update logs can also be used to improve thequality of recommendations by testing assumptions about the benefitsprovided by the recommendations. For example, answers to severalscenarios may be informative to improve the quality of recommendations:What are the impact/effects of movement recommendations, that is, how dothey affect service providers' driving experience over time? Whatbenefits do service providers actually see following movementrecommendations? What happens if they choose not to follow? Does thisincrease their ATR (actual time to request), or lower their ATR? What'sthe general distribution of ATR for service providers who follow/ignorerecommendations? If service providers are following, do serviceproviders get dispatches along the way, or only once they arrive at thetarget? Given a time range, how many of service providers targeted bymovement recommendation arrived at the target? After arriving at thetarget, do they get dispatches, or wait for a long time then go offline?Is the ranking logic for choosing recommendations based on solidassumptions, that is, is ETA accurate? If service providers arefollowing recommendations and arrive at the target, what's thedifference between ATA (actual time to arrival) and initial ETA? What'sthe distribution of initial ETA on sending recommendations to serviceproviders? Are movement recommendations acting as expected? How does thedistribution of service providers targeted change after movementrecommendations are sent? How are metrics (e.g. eyeball ETA) on riderside affected?

In another example, the progress output processing service 160 candetermine if a service provider is progressing toward the pickuplocation once they accept a service request, which can be used to detectservice provider fraud and attempts to game the system. For example, aservice provider may accept a service request but have no intention ofpicking up the user so that they can get a cancellation fee. To preventsuch fraud, any cancellation fee can be waived for the user if theprogress detection system detects that the service provider was ignoringthe pickup. In other cases, the fare can be adjusted if the serviceprovider was not making sufficient progress towards the destination on aride.

In some use cases, the progress output processing service 160 cangenerate a warning to be sent to the service provider indicating thatthe service provider is not progressing towards the target. Thesewarnings can be displayed on a user interface of the transportapplication 191 running on the service provider device 190.

FIGS. 2A and 2B are block diagrams illustrating an example taskregistration architecture. In some implementations, a progress detectionengine 200 supports an online or offline registration process through aremote procedure call (RPC) interface to register either a single taskor tasks in bulk.

In some aspects, customer services, executing as part of the on-demandarrangement service managed by the network computer system 100, transmitregistration requests to the registration module 210 of the progressdetection engine 200. Each of the registration requests can include aset of requirements, which the components of the progress detectionengine 200 use to build the attraction tasks for one or more providers.Based on task configuration, attraction states of different measurementmodels and task-based attraction signal entities are createdaccordingly.

In some examples, a provider can have multiple active attraction tasksat the same time, each of which can have different target locations.These attraction tasks can also differ by function and the models usedto create the tasks. For example, a provider may have one attractiontask created using a heatmap-based algorithm, such as described in FIG.2A, and another attraction task created using a heuristic algorithm,such as described in FIG. 2B.

In some aspects, a service generating heatmaps sends a refreshed heatmapto the progress detection engine 200 periodically (e.g., every twominutes). In one implementation, a heatmap represents a city,metropolitan area, or other geographical region, and is composed ofkey-value pairs of hexagonally shaped sub-regions. After receiving therefreshed heatmap, the POI module 220 determines heatmap-based points ofinterest and stores the centroid latitude and longitude coordinates ofthe points of interest in the database 245.

Based on the points of interest and the set of requirements in thereceived request, the registration module 210 sets up attraction tasksfor progress measurement for the task manager 230 and signal module 240.For example, the registration module 210 can create, for each of thepoints of interest, an attraction task with a target locationcorresponding to that point of interest.

In one implementation, the POI module 220 uses the heatmaps to selecttwo measurement destinations: a local maximum hexagon (i.e., sub-regionof the heatmap), and a global maximum hexagon. In some aspects, thelocal maximum hexagon represents a sub-region near the location of theprovider with a higher dynamic score (i.e., high demand relative tosupply) compared to other nearby sub-regions. The global maximum hexagonrepresents the sub-region with the highest dynamic score on the heatmap.

To determine the local maximum hexagon, the POI module 220 can performthe following algorithm (where, in one implementation, the PeakZonerepresents hexagons with the highest scores and the SurgeHill representsan area of contiguous hexagons, centered on the PeakZone, with flat ordecreasing scores):

Sort the dynamic scores from highest to lowest into a list S;

-   -   While S is not empty:    -   Set the highest dynamic score H_(max) as the PeakZone;    -   Expand this PeakZone to all neighboring hexagons that have a        dynamic score equal to H_(max);    -   Create a Marker at the center of PeakZone;    -   Continue to expand SurgeHill outward by moving to neighboring        hexagons with equal or lower dynamic scores;    -   Remove SurgeHill from S.

FIG. 2B illustrates components of the progress detection engine 200 thatcan create an attraction task using a heuristic algorithm. A providerevent update module 250 receives data corresponding to an event updatefor a provider. If the provider is still in a begin state, the shadowmodule 260 can create one or more attraction tasks for the provider foreach point of interest that the POI module 260 identifies using aheuristic algorithm.

In some aspects, the POI module 220 uses various signals and metrics toforecast supply and demand for the region in which the provider islocated. Based on this forecast, the POI module 220 can generate one ormore heuristic points of interest. The shadow module 260 can then createattraction tasks for the provider for each of the points of interest,and the signal module 240 can create task-based attraction signals. Theattraction tasks and signals can be stored in the database 245 for laterretrieval during task processing. In addition, in contrast to theheatmap-based algorithm, the heuristic algorithm can predict where theprovider may go without actively showing them a recommendation.

FIG. 3 is a block diagram illustrating an example task processingarchitecture. In some aspects, the progress detection engine 300provides session-based measurement for registered attraction tasks. Ameasurement session starts when a provider becomes open (e.g., theprovider comes online, after trip completion, cancellation, orexpiration), and ends by configuration of the attraction taskregistered, which includes events such as the provider going offline,accepting a request, and expiration of the task, which can be defined bythe end time of a task. In some examples, a session starts when a newattraction task is created, such as on refresh of the local heatmap.

In some aspects, the progress detection engine 300 processes attractiontasks based on event updates and provider location updates per hexagon.A provider event update module 350 processes the provider event updates,and a provider location update module 310 processes provider locationupdates. This processing can determine whether the updates are valid orinvalid. The core logic of attraction measurement is shared betweenthese event and location updates.

On receiving valid updates for a provider, the progress detection engine300 retrieves active attraction tasks associated with the provider. Onone path, these tasks and real-time stats are passed to a signal module340 to update. The signal module 340 gets and updates attractionsignals, including, among other signals, whether the provider hasreceived a trip request, whether the provider has accepted the request,whether the provider has arrived at a target location, and a distanceresult. In some aspects, the signal module 340 keeps track ofsession-based stats related to attraction tasks.

On another path, provider updates are also passed to the task manager330 to measure provider progress/attraction. For each attraction task,the task manager 330 retrieves associated attraction states from thedatabase 345 then uses a dispatcher module 332 to dispatch eachattraction state to a uETA pipeline worker 362 based on taskconfiguration. Each uETA pipeline worker 362 is responsible of handlingstate transitions of a specific measurement model and sending statisticsto corresponding logging functions. As an example, a uETA pipelineworker 362 is responsible of handling attraction state transitions ofthe shrinking circle attraction model illustrated in FIG. 4.

In some aspects, a provider's attraction towards a target location canbe measured by multiple models at the same time, and different modelscan have their own mechanisms for state management when a providerevent/location update happens. In the progress detection engine 300, anattraction state is used to track state transitions of each model acrossthe lifecycle of an attraction task. Based on the task configuration, anattraction task can be measured by multiple types of models, and eachmodel may have multiple sets of parameters. Thus an attraction task canhave multiple associated attraction states.

In further aspects, the task manager 300 also implements a reducermodule 334 to support aggregated attraction analysis over multiple POIswhen a session ends. A new pipeline worker 364 can pipe computedattraction scores to logging functions for offline analysis andvisualization.

FIG. 4 is a block diagram illustrating a shrinking circle attractionmodel used for real-time progress monitoring. In some implementations, aprogress detection engine operates as a backend service that monitorswhether a service provider is making progress towards a target (e.g., apick up or drop off location) according to the shrinking circleattraction model. Upon processing service provider location or servicestate updates, the progress detection engine uses the service provider'scurrent attraction state, initial and current estimated times to arrival(ETA) to the target, and duration of time elapsed to determine whetherthe service provider remains in the current attraction state or moves toa different attraction state. In some examples, a service provider canbe in one of five states: initial 410, toward 420, ignore 430, inside440, and completed 450.

In one implementation, the progress detection engine determines theinitial ETA to the target of the service instruction from a startingposition of a computing device of a service provider within a geographicarea. For example, a service provider may receive a movementrecommendation to travel to a target location that is estimated to be 10minutes away from the service provider's starting location. Uponcreation of the movement recommendation task, the service provider isconsidered to be in the initial attraction state 410.

The progress detection engine remotely monitors the computing device ofthe service provider to receive provider data corresponding to thecurrent position of the computing device as the service provider travelswithin the geographic area, whether towards or away from the targetlocation. In some aspects, whenever the service provider crosses fromone sub-region to another or after the passage of 60 seconds where theservice provider remains in the same sub-region, the progress detectionengine calculates a duration of time that has passed since the initialETA was estimated and estimates a remaining ETA based on the expectedtravel time from the service provider's current position to the target.The progress detection engine then checks a set of progress conditionsto determine whether to move the service provider to a differentattraction state.

As illustrated in FIG. 4, one example for an attraction model tracking a“pickup” task implements six progress conditions that the progressdetection engine checks for:

1. remETA+Duration−InitialETA<=Max (2 min, buffer*InitialETA);

2. remETA+Duration−InitialETA>Max (2 min, buffer*InitialETA);

3. Service provider inside target geo;

4. Service provider outside target geo;

5. Service provider transits to the state of “Pickup”; and

6. Service provider transits to the state of “Cancellation.”

For condition #1, if the remaining ETA plus duration elapsed minusinitial ETA is equal to or below a threshold of the greater of 2 minutesor the initial ETA times a predetermined buffer value, the serviceprovider is considered to be progressing towards the target. The buffervalue is a number between 0 and 1 that is chosen to allow a margin offlexibility to the ETA calculations that the model uses since it ispossible that factors such as traffic or weather conditions have changedsince the initial ETA was calculated. The buffer value can be setdifferently for different geographic areas and can be updated based ondetermined rates of false positives and false negatives.

Using a buffer value of 0.6, if the service provider's initial ETA was10 minutes and upon an update after 5 minutes with a remaining ETA of 7minutes, the service provider would be considered to be progressingtowards the target since (7+5−10) is less than (10*0.6). As illustrated,service providers meeting condition #1 in the initial attraction state410 transition to the toward attraction state 420. Service providersmeeting condition #1 in the toward attraction state 420 remain there.

For condition #2, if the remaining ETA plus duration elapsed minusinitial ETA is above a threshold of the greater of 2 minutes or theinitial ETA times a predetermined buffer value, the service provider isconsidered to be ignoring the target. As illustrated, service providersmeeting condition #2 in the initial attraction state 410 transition tothe ignore attraction state 430. Service providers meeting condition #2in the ignore attraction state 430 remain there. In someimplementations, once a service provider is in the ignore attractionstate 430, the service provider cannot transition to the towardattraction state 420 even if they meet condition #1.

For condition #3, the service provider transitions to the insideattraction state 440 if they are detected within the same geo, orsub-region, as the target location.

For condition #4, the service provider transitions out of the insideattraction state 440 if they are detected outside the same geo, orsub-region, as the target location. This can result in the serviceprovider transitioning to either the toward attraction state 420 or theignore attraction state 430 based on conditions #1 or #2.

Conditions #5 and #6 check whether the service state of the serviceprovider has changed to indicate that the service provider has picked upthe user or whether the ride request was cancelled. In either case, theservice provider transitions to the completed attraction state 450. Inother examples, as in for a movement recommendation task, conditions #5and #6 can check for the service provider entering an “accepted” or “offduty” state, respectively.

The progress detection engine can also implement a compliance modelbased on the attraction states. In one example, if the service providerreaches the completed attraction state 450 through the ignore attractionstate 430, the compliance level is set to “failure.” When the serviceprovider reaches the ignore attraction state 430, if the sessionduration is less than the initial ETA, the compliance level is set to“warning” the first time the condition is met. If the session durationis equal to or greater than the initial ETA, the compliance level is setto “failure.”

In addition to the scaling circle attraction model illustrated, theprogress detection engine can implement other models with differentpreset conditions, such as service provider needing to move 0.55 mileswithin the initial 60 seconds, as separate models to detect a serviceprovider's progress towards the target location.

Methodology

FIG. 5 is a flow chart describing an example method of real-timeprogress monitoring for a transport provider, according to examplesdescribed herein. FIG. 6 is a flow chart describing an example method ofprogress monitoring in a network computer system, according to examplesdescribed herein. Although some operations of the methods are describedbelow as being performed by specific components of the computer systemsillustrated in FIG. 1, it will be appreciated that these operations neednot necessarily be performed by the specific components identified, andcould be performed by a variety of components and modules, potentiallydistributed over a number of machines. Accordingly, references may bemade to elements of the network computer system 100 for the purpose ofillustrating suitable components or elements for performing a step orsub step being described. Alternatively, at least certain ones of thevariety of components and modules described in the network computersystem 100 can be arranged within a single hardware, software, orfirmware component. It will also be appreciated that some of the stepsof these methods may be performed in parallel or in a different orderthan illustrated.

In the method described in FIG. 5, the network computer system provides,to a computing device of a service provider over a network, a serviceinstruction (510). The service instruction can include offers, such as aservice service request to pick up and transport a user, andrecommendations, such as a movement recommendation encouraging theservice provider to relocate to another geographic area that isdetermined to offer a superior experience for the service provider(e.g., lower idle time, more revenue, etc.).

The network computer system remotely monitors the computing device toreceive provider data corresponding to a current position of thecomputing device, as determined by a location-based resource of thecomputing device, as the service provider travels within a geographicarea. In addition, the network computer system remotely monitors thecomputing device to receive provider data corresponding to a servicestate of the service provider (e.g., open, occupied, offline, etc.)(520).

The network computer system periodically determines whether the serviceprovider is making progress towards a target (e.g., a pick up location,movement recommendation location) of the service instruction based onthe current position of the computing device and a set of progressconditions (530). Responsive to a change in the service state of theservice provider, the network computer system determines whether theservice provider satisfied the set of progress conditions (540).

In the method described in FIG. 6, a task service can register tasks totrack the progress of any movement recommendations sent to serviceproviders or to track the progress of a service provider towards a pickup or drop off location. In addition, a progress detection engine candetermine whether the task should be monitored via per-providerattraction (e.g., offer) or citywide attraction (e.g., hexcentives), andcreate attraction/initial attraction states accordingly (610).

In one implementation, to track progress in real-time, the progressdetection engine reads logs of service provider location and servicestate that are processed by upstream services (620). On receiving alocation update of a service provider, the progress detection engineloads existing attractions related to the service provider from adatabase, and for each attraction task, updates the attraction statesbased on the estimated time to arrival and time elapsed (630). In someexamples, the progress detection engine updates attraction states eachtime the service provider crosses from one sub-region to another orevery 60 seconds if the service provider remains within one sub-regionduring that time.

The progress detection engine can process compliance updates based on acompliance model using the attraction states (640). In one example, ifthe service provider reaches the completed attraction state through theignore attraction state, the compliance level is set to “failure.” Whenthe service provider reaches the ignore attraction state, if the sessionduration is less than the initial ETA, the compliance level is set to“warning” the first time the condition is met. If the session durationis equal to or greater than the initial ETA, the compliance level is setto “failure.”

In some use cases, a progress output processing service can generate awarning to be sent to the service provider indicating that the serviceprovider is not progressing towards the target (642). These warnings canbe displayed on a user interface of the transport application running onthe service provider device.

The progress output processing service can determine whether serviceproviders are following repositioning recommendations while online andnot in-service. The progress detection system further provides afeedback loop for movement recommendations to service providers toimprove supply provisioning. For example, the progress detection systemcan determine what percent of service providers are following arecommendation, and if too many or too few are following, the supplyprovisioning service can adjust accordingly (644).

If the service provider's service state changes to indicate that a giventask (e.g., pick up, drop off, movement recommendation) is complete(whether successful or canceled), the progress detection system stopstracking the service provider and can update the compliance status as“successful” or “failure” (650). The progress detection system logs therelevant information, including the task, a timestamp, the serviceprovider ID, and the compliance status (660).

Service Provider Device

FIG. 7 is a block diagram illustrating an example computing deviceexecuting and operating a designated transport application forcommunicating with a network transport service, according to examplesdescribed herein. In many implementations, the service provider device780 can comprise a mobile computing device, such as a smartphone, tabletcomputer, laptop computer, VR or AR headset device, and the like. Assuch, the service provider device 780 can include typical telephonyfeatures such as a microphone 745, a camera 750, and a communicationinterface 710 to communicate with external entities using any number ofwireless communication protocols. In certain aspects, the serviceprovider device 780 can store a designated application (e.g., a serviceprovider application 732) in a local memory 730. In many aspects, theservice provider device 780 further stores information corresponding toa contacts list 734 and calendar appointments 736 in the local memory730. In variations, the memory 730 can store additional applicationsexecutable by one or more processors 740 of the service provider device780, enabling access and interaction with one or more host servers overone or more networks 760.

In response to user input, the service provider application 732 can beexecuted by a processor 740, which can cause an application interface tobe generated on a display screen 720 of the service provider device 780.The application interface can enable the service provider to, forexample, check current price levels and availability for the on-demandarrangement service. In various implementations, the applicationinterface can further enable the service provider to select frommultiple ride service types, such as a carpooling service type, aregular ride-sharing service type, a professional ride service type, avan on-demand service type, a luxurious ride service type, and the like.

The provider can enter various states or modes, such as an online modeor a pause state via user inputs provided on the application interface.For example, the provider can choose a duration for the pause state andselect a facility from a recommended list of facilities to accommodatethe provider during the pause state. As provided herein, the serviceprovider application 732 can further enable a communication link with anetwork computer system 700 over the network 760, such as the networkcomputer system 100 as shown and described with respect to FIG. 1.Furthermore, as discussed herein, the service provider application 732can display requester information on the application interface thatincludes data regarding a service requester so that the provider canchoose whether to accept or reject a service invitation received fromthe network computer system 700.

The processor 740 can transmit the provider status (i.e., modes theprovider is in, or the service state) via a communications interface 710to the backend network computer system 700 over a network 760. Invarious examples, the service provider device 780 can further include aGPS module 755, which can provide location data indicating the currentlocation of the provider to the network computer system 700 to select anappropriate service provider to fulfill user service requests. Inalternative aspects, hard-wired circuitry may be used in place of or incombination with software instructions to implement aspects describedherein. Thus, aspects described are not limited to any specificcombination of hardware circuitry and software.

Hardware Diagram

FIG. 8 is a block diagram that illustrates a computer system upon whichexamples described herein may be implemented. A computer system 800 canbe implemented on, for example, a server or combination of servers. Forexample, the computer system 800 may be implemented as part of a networkservice for providing on-demand services. In the context of FIG. 1, thenetwork computer system 100 may be implemented using a computer system800 such as described by FIG. 8. The network computer system 100 mayalso be implemented using a combination of multiple computer systems asdescribed in connection with FIG. 8.

In one implementation, the computer system 800 includes processingresources 810, a main memory 820, a read-only memory (ROM) 830, astorage device 840, and a communication interface 850. The computersystem 800 includes at least one processor 810 for processinginformation stored in the main memory 820, such as provided by a randomaccess memory (RAM) or other dynamic storage device, for storinginformation and instructions which are executable by the processor 810.The main memory 820 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by the processor 810. The computer system 800 may also includethe ROM 830 or other static storage device for storing staticinformation and instructions for the processor 810. A storage device840, such as a magnetic disk or optical disk, is provided for storinginformation and instructions.

The communication interface 850 enables the computer system 800 tocommunicate with one or more networks (e.g., a cellular network) throughuse of the network link (wireless or wired). Using the network link, thecomputer system 800 can communicate with one or more computing devices,one or more servers, and/or one or more self-driving vehicles. Inaccordance with some examples, the computer system 800 receives servicerequests from mobile computing devices of individual users. Theexecutable instructions stored in the memory 830 can include progressmonitoring instructions 824 to perform one or more of the methodsdescribed herein when executed.

By way of example, the instructions and data stored in the memory 820can be executed by the processor 810 to implement an example networkcomputer system 100 of FIG. 1. In performing the operations, theprocessor 810 can handle service requests and provider statuses andsubmit service invitations to facilitate fulfilling the servicerequests. The processor 810 executes instructions for the softwareand/or other logic to perform one or more processes, steps and otherfunctions described with implementations, such as described by FIGS. 1through 5.

Examples described herein are related to the use of the computer system800 for implementing the techniques described herein. According to oneexample, those techniques are performed by the computer system 800 inresponse to the processor 810 executing one or more sequences of one ormore instructions contained in the main memory 820. Such instructionsmay be read into the main memory 820 from another machine-readablemedium, such as the storage device 840. Execution of the sequences ofinstructions contained in the main memory 820 causes the processor 810to perform the process steps described herein. In alternativeimplementations, hard-wired circuitry may be used in place of or incombination with software instructions to implement examples describedherein. Thus, the examples described are not limited to any specificcombination of hardware circuitry and software.

It is contemplated for examples described herein to extend to individualelements and concepts described, independently of other concepts, ideasor systems, as well as for examples to include combinations of elementsrecited anywhere in this application. Although examples are described indetail herein with reference to the accompanying drawings, it is to beunderstood that the concepts are not limited to those precise examples.As such, many modifications and variations will be apparent topractitioners skilled in this art. Accordingly, it is intended that thescope of the concepts be defined by the following claims and theirequivalents. Furthermore, it is contemplated that a particular featuredescribed either individually or as part of an example can be combinedwith other individually described features, or parts of other examples,even if the other features and examples make no mention of theparticular feature. Thus, the absence of describing combinations shouldnot preclude claiming rights to such combinations.

What is claimed is:
 1. A network computer system for managing anon-demand transport service, comprising: one or more processors; and amemory storing instructions that, when executed by the one or moreprocessors, cause the network computer system to perform operationscomprising: providing a service instruction to a computing device of aservice provider over a network; remotely monitoring the computingdevice to receive service provider data corresponding to (i) a currentposition of the computing device, as determined by a location-basedresource of the computing device, as the service provider travels withina geographic area, and (ii) a service state of the service provider; andperiodically determining whether the service provider is making progresstowards a target of the service instruction based on the serviceprovider data and a set of progress conditions, including determiningwhether the service provider satisfied the set of progress conditions inresponse to a change in the service state of the service provider. 2.The network computer system of claim 1, wherein periodically determiningwhether the service provider is making progress towards a target of theservice instruction based on the current position of the computingdevice and a set of progress conditions includes: determining an initialestimated time to arrival (ETA) to the target of the service instructionfrom a starting position of the computing device of the serviceprovider; updating (i) a remaining ETA to the target from the currentposition of the computing device, and (ii) a duration of time elapsedsince the initial ETA was determined; and comparing the remaining ETA,the duration of time elapsed, and the initial ETA to a predeterminedthreshold to determine whether the service provider is progressingtowards the target.
 3. The network computer system of claim 2, whereinthe service provider is determined to be progressing towards the targetwhen the duration of time elapsed added to the difference between theremaining ETA and the initial ETA is below the predetermined threshold.4. The network computer system of claim 2, wherein the initial ETA isbased at least on a distance between the starting position of thecomputing device of the service provider and the target and trafficconditions in the geographic area.
 5. The network computer system ofclaim 1, further comprising: flagging the service provider as beingwithin an attraction state of a plurality of attraction states based onthe set of progress conditions.
 6. The network computer system of claim5, further comprising: determining a compliance with the serviceinstruction based on the attraction state of the service provider. 7.The network computer system of claim 1, further comprising instructionsto perform operations comprising: selectively displaying content on auser interface of the computing device based on whether the serviceprovider satisfied the set of progress conditions.
 8. The networkcomputer system of claim 7, wherein the content is a warning regardingthe service instruction.
 9. A method of managing an on-demand transportservice, the method being implemented by one or more processors andcomprising: providing a service instruction to a computing device of aservice provider over a network; remotely monitoring the computingdevice to receive service provider data corresponding to (i) a currentposition of the computing device, as determined by a location-basedresource of the computing device, as the service provider travels withina geographic area, and (ii) a service state of the service provider; andperiodically determining whether the service provider is making progresstowards a target of the service instruction based on the serviceprovider data and a set of progress conditions, including determiningwhether the service provider satisfied the set of progress conditions inresponse to a change in the service state of the service provider. 10.The method of claim 9, wherein periodically determining whether theservice provider is making progress towards a target of the serviceinstruction based on the current position of the computing device and aset of progress conditions includes: determining an initial estimatedtime to arrival (ETA) to the target of the service instruction from astarting position of the computing device of the service provider;updating (i) a remaining ETA to the target from the current position ofthe computing device, and (ii) a duration of time elapsed since theinitial ETA was determined; and comparing the remaining ETA, theduration of time elapsed, and the initial ETA to a predeterminedthreshold to determine whether the service provider is progressingtowards the target.
 11. The method of claim 10, wherein the serviceprovider is determined to be progressing towards the target when theduration of time elapsed added to the difference between the remainingETA and the initial ETA is below the predetermined threshold.
 12. Themethod of claim 10, wherein the initial ETA is based at least on adistance between the starting position of the computing device of theservice provider and the target and traffic conditions in the geographicarea.
 13. The method of claim 9, further comprising: flagging theservice provider as being within an attraction state of a plurality ofattraction states based on the set of progress conditions.
 14. Themethod of claim 13, further comprising: determining a compliance withthe service instruction based on the attraction state of the serviceprovider.
 15. The method of claim 9, further comprising instructions toperform operations comprising: selectively displaying content on a userinterface of the computing device based on whether the service providersatisfied the set of progress conditions.
 16. The method of claim 15,wherein the content is a warning regarding the service instruction. 17.A non-transitory computer-readable medium that stores instructions,executable by one or more processors, to cause the one or moreprocessors to perform operations comprising: providing a serviceinstruction to a computing device of a service provider over a network;remotely monitoring the computing device to receive service providerdata corresponding to (i) a current position of the computing device, asdetermined by a location-based resource of the computing device, as theservice provider travels within a geographic area, and (ii) a servicestate of the service provider; and periodically determining whether theservice provider is making progress towards a target of the serviceinstruction based on the service provider data and a set of progressconditions, including determining whether the service provider satisfiedthe set of progress conditions in response to a change in the servicestate of the service provider.
 18. The non-transitory computer-readablemedium of claim 17, wherein periodically determining whether the serviceprovider is making progress towards a target of the service instructionbased on the current position of the computing device and a set ofprogress conditions includes: determining an initial estimated time toarrival (ETA) to the target of the service instruction from a startingposition of the computing device of the service provider; updating (i) aremaining ETA to the target from the current position of the computingdevice, and (ii) a duration of time elapsed since the initial ETA wasdetermined; and comparing the remaining ETA, the duration of timeelapsed, and the initial ETA to a predetermined threshold to determinewhether the service provider is progressing towards the target.
 19. Thenon-transitory computer-readable medium of claim 18, wherein the serviceprovider is determined to be progressing towards the target when theduration of time elapsed added to the difference between the remainingETA and the initial ETA is below the predetermined threshold.
 20. Thenon-transitory computer-readable medium of claim 18, wherein the initialETA is based at least on a distance between the starting position of thecomputing device of the service provider and the target and trafficconditions in the geographic area.